Actuatable occupant restraining systems having an inflatable air bag in vehicles are known in the art. Such systems that are controlled in response to whether the seat is occupied, an object on the seat is animate or inanimate, a rearward facing child seat present on the seat, and/or in response to the occupant's position, weight, size, etc., are referred to as smart restraining systems. One example of a smart actuatable restraining system is disclosed in U.S. Pat. No. 5,330,226.
Pattern recognition systems include systems capable of distinguishing between classes of real world stimuli according to a plurality of distinguishing characteristics, or features, associated with the classes. A number of pattern recognition systems are known in the art, including various neural network classifiers, support vector machines, and Bayesian classification models. Training a pattern recognition system requires a large number of samples to obtain acceptable accuracy rates. In some applications, samples will not be available in sufficient number for some or all of the output classes. Even where samples are available in sufficient numbers, collecting and preparing the samples can be a significant expense, especially where the output classes for a particular application change frequently. Further, training a pattern recognition classifier is a time-intensive process that must be repeated with the addition of each new output class.